scholarly journals Stochastic analysis using public data for forecasting of epidemic spreading of the novel coronavirus disease

2020 ◽  
Author(s):  
Leonardo dos Santos Lima
Keyword(s):  
Stochastic Model ◽  
Stochastic Analysis ◽  
Planck Equation ◽  
The Novel ◽  
Epidemic Spreading ◽  
Public Data ◽  
Health Agencies ◽  
The Mean ◽  
Novel Coronavirus ◽  
Official Data

Abstract We propose a stochastic model for epidemic spreading of the novel coronavirus based in data supported by the Brazilian health agencies. Furthermore, we performed an analysis using the Fokker-Planck equation estimating the novel cases in the day t as the mean half-width of the distribution of novel cases P(N,t). Our results display that the model based in the Itô diffusion adjusts well to the results supplied by health Brazilian agencies due to large uncertain in the official data and to the low number of tests realized in the population.

scholarly journals Dynamics based on analysis of public data for spreading of disease

2021 ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Planck Equation ◽  
The Novel ◽  
Epidemic Spreading ◽  
Data Set ◽  
Public Health Agencies ◽  
The Public ◽  
Public Data ◽  
Health Agencies ◽  
Novel Coronavirus ◽  
Official Data

Abstract The stochastic model for epidemic spreading of the novel coronavirus disease based on the data set supported by the public health agencies in countries as Brazil, EUA and India is investigated. We perform the numerical analysis using the stochastic differential equation in Itô’s calculus (SDE) for the estimating of novel cases daily as well as analytical calculations solving the correspondent Fokker-Planck equation for the density probability distribution of novel cases, P(N(t); t). Our results display that the model based in the Itô diffusion fits well to the results due to uncertain in the official data and to the number of tests realized in the populations of each country.

scholarly journals Modeling of spreading of the novel coronavirus based in the stochastic dynamic

2020 ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Differential Equation ◽  
Mathematical Model ◽  
Stochastic Model ◽  
Planck Equation ◽  
Stochastic Dynamic ◽  
The Novel ◽  
Model Based ◽  
The Mean ◽  
Novel Coronavirus ◽  
Official Data

Abstract In this paper, one proposes a stochastic model based on Itô diffusion as mathematical model for time evolution of novel cases N(t) of the SARS-CoV-2 (COVID-19) in each day t. I propose a correspondent stochastic differential equation (SDE) analogous to classical differential equation for epidemic growing for some diseases as smallpox and typhoid fever. Furthermore, we made an analysis using the Fokker-Planck equation giving an estimating of the novel cases in the day t as the mean half-width of the distribution P(N,t) of novel cases. My results display that the model based on Itô diffusion fits well to the results supported by healthy Brazilian agencies due to large uncertainly in the official data generated by the low number of tests realized generating so a strong randomness in the official data.

scholarly journals Dynamics based on analysis of public data for spreading of disease

2021 ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Planck Equation ◽  
Probability Density Distribution ◽  
The Novel ◽  
Epidemic Spreading ◽  
Data Set ◽  
Public Health Agencies ◽  
The Public ◽  
Public Data ◽  
Health Agencies ◽  
Novel Coronavirus

Abstract The stochastic model for epidemic spreading of the novel coronavirus disease based on the data set supply by the public health agencies in countries as Brazil, United States and India is investigated. We perform a numerical analysis using the stochastic differential equation in Itô’s calculus for the estimating of novel cases daily, as well as analytical calculations solving the correspondent Fokker-Planck equation for the probability density distribution of novel cases, P(N(t); t). Our results display that the model based in the Itô’s diffusion fits well to the results due to uncertainty in the official data and to the number of testsrealized in populations of each country.

scholarly journals Dynamics based on analysis of public data for spreading of disease

2020 ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Public Health ◽  
The Novel ◽  
Epidemic Spreading ◽  
Data Set ◽  
Public Health Agencies ◽  
The Public ◽  
Public Data ◽  
Health Agencies ◽  
Novel Coronavirus ◽  
Official Data

Abstract The stochastic model for epidemic spreading of the novel coronavirus disease based on the data set supported by the public health agencies in countries as Brazil, EUA and India is investigated. We performed the numerical analysis using the stochastic differential equation for estimating of the novel cases diary as well as analytical calculations solving the correspondent partial equation for the distribution of novel cases P. Our results display that the model based in the Itô diffusion fits well to the results diary due to uncertain in the official data and to the number of tests realized in the populations of each country.

scholarly journals Dynamics based on analysis of public data for spreading of disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Planck Equation ◽  
Probability Density Distribution ◽  
The Novel ◽  
Epidemic Spreading ◽  
Data Set ◽  
Public Health Agencies ◽  
The Public ◽  
Public Data ◽  
Health Agencies ◽  
Novel Coronavirus

AbstractThe stochastic model for epidemic spreading of the novel coronavirus disease based on the data set supply by the public health agencies in countries as Brazil, United States and India is investigated. We perform a numerical analysis using the stochastic differential equation in Itô’s calculus for the estimating of novel cases daily, as well as analytical calculations solving the correspondent Fokker–Planck equation for the probability density distribution of novel cases, P(N(t), t). Our results display that the model based in the Itô’s diffusion fits well to the results due to uncertainty in the official data and to the number of tests realized in populations of each country.

scholarly journals Modeling and forecasting of epidemic spreading of the SARS-CoV-2 based in the nonlinear stochastic dynamics

2020 ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Differential Equation ◽  
Mathematical Model ◽  
Stochastic Dynamics ◽  
Planck Equation ◽  
Epidemic Spreading ◽  
Model Based ◽  
Modeling And Forecasting ◽  
The Mean ◽  
Nonlinear Stochastic Dynamics ◽  
Official Data

Abstract In this paper, we propose a stochastic model based on Itô diffusion as mathematical model for time evolution of new cases N(t) of the SARS-CoV-2 (COVID-19) in each day t. We propose a correspondent stochastic differential equation (SDE) analogs to classical differential equations for epidemic growing for some diseases as smallpox and typhoid fever. Furthermore, we made an analysis using the Fokker-Planck equation giving an estimating of the new cases in each day t as the mean half-width of the distribution P(N,t) of new cases. Our results display that the model based on Itô diffusion fit well to the results supported by healthy Brazilian agencies due to large uncertain in the official results and to the low number of tests realized generating so a strong randomness in the official data.

Curve Estimation Modeling for Predictions of the Novel Coronavirus (Covid-19) Epidemic in Nigeria

2020 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Christian Ebere Enyoh ◽  
Andrew Wirnkor Verla ◽  
Chidi Edbert Duru ◽  
Emmanuel Chinedu Enyoh ◽  
Budi Setiawan
Keyword(s):  
State Governments ◽  
The Novel ◽  
Curve Estimation ◽  
Exponential Models ◽  
Inverse Models ◽  
Forward Prediction ◽  
S Curve ◽  
Novel Coronavirus ◽  
Best Fit ◽  
Official Data

Based on the official Nigeria Centre for Disease Control (NCDC) data, the current research paper modeled the confirmed cases of the novel coronavirus disease 2019 (COVID-19) in Nigeria. Ten different curve regression models including linear, logarithmic, inverse, quadratic, cubic, compound, power, S-curve, growth, and exponential were used to fit the obtained official data. The cubic (R2 = 0.999) model gave the best fit for the entire country. However, the growth and exponential had the lowest standard error of estimate (0.958) and thus may best be used. The equations for these models were e0.78897+0.0944x and 2.2011e0.0944x respectively. In terms of confirmed cases in individual State, quadratic, cubic, compound, growth, power and exponential models generally best describe the official data for many states except for the state of Kogi which is best fitted with S-curve and inverse models.  The error between the model and the official data curve is quite small especially for compound, power, growth and exponential models. The computed models will help to realized forward prediction and backward inference of the epidemic situation in Nigeria, and the relevant analysis help Federal and State governments to make vital decisions on how to manage the lockdown in the country.

scholarly journals The Impact of Armed Conflict on the Epidemiological Situation of COVID-19 in Libya, Syria and Yemen

2021 ◽  
Vol 9 ◽  
Author(s):  
Mohamed A. Daw
Keyword(s):  
Armed Conflict ◽  
Early Stage ◽  
Armed Conflicts ◽  
Cumulative Number ◽  
The Novel ◽  
Global Pandemic ◽  
Large Numbers ◽  
Public Data ◽  
Novel Coronavirus ◽  
The Impact

Background: Since the Arab uprising in 2011, Libya, Syria and Yemen have gone through major internal armed conflicts. This resulted in large numbers of deaths, injuries, and population displacements, with collapse of the healthcare systems. Furthermore, the situation was complicated by the emergence of COVID-19 as a global pandemic, which made the populations of these countries struggle under unusual conditions to deal with both the pandemic and the ongoing wars. This study aimed to determine the impact of the armed conflicts on the epidemiology of the novel coronavirus (SARS-CoV-2) within these war-torn countries and highlight the strategies needed to combat the spread of the pandemic and its consequences.Methods: Official and public data concerning the dynamics of the armed conflicts and the spread of SARS-COV-2 in Libya, Syria and Yemen were collected from all available sources, starting from the emergence of COVID-19 in each country until the end of December 2020. Datasets were analyzed by a set of statistical techniques and the weekly resolved data were used to probe the link between the intensity levels of the conflict and the prevalence of COVID-19.Results: The data indicated that there was an increase in the intensity of the violence at an early stage from March to August 2020, when it approximately doubled in the three countries, particularly in Libya. During that period, few cases of COVID-19 were reported, ranging from 5 to 53 cases/day. From September to December 2020, a significant decline in the intensity of the armed conflicts was accompanied by steep upsurges in the rate of COVID-19 cases, which reached up to 500 cases/day. The accumulative cases vary from one country to another during the armed conflict. The highest cumulative number of cases were reported in Libya, Syria and Yemen.Conclusions: Our analysis demonstrates that the armed conflict provided an opportunity for SARS-CoV-2 to spread. The early weeks of the pandemic coincided with the most intense period of the armed conflicts, and few cases were officially reported. This indicates undercounting and hidden spread during the early stage of the pandemic. The pandemic then spread dramatically as the armed conflict declined, reaching its greatest spread by December 2020. Full-blown transmission of the COVID-19 pandemic in these countries is expected. Therefore, urgent national and international strategies should be implemented to combat the pandemic and its consequences.

scholarly journals Transmission potential of the novel coronavirus (COVID-19) onboard the Diamond Princess Cruises Ship, 2020

2020 ◽  
Author(s):  
Kenji Mizumoto ◽  
Gerardo Chowell
Keyword(s):  
Mathematical Modeling ◽  
Time Series ◽  
Reproduction Number ◽  
Transmission Dynamics ◽  
The Novel ◽  
Epidemic Threshold ◽  
Transmission Potential ◽  
Incidence Data ◽  
The Mean ◽  
Novel Coronavirus

AbstractAn outbreak of COVID-19 developed aboard the Princess Cruises Ship during January-February 2020. Using mathematical modeling and time-series incidence data describing the trajectory of the outbreak among passengers and crew members, we characterize how the transmission potential varied over the course of the outbreak. Our estimate of the mean reproduction number in the confined setting reached values as high as ∼11, which is higher than mean estimates reported from community-level transmission dynamics in China and Singapore (approximate range: 1.1-7). Our findings suggest that Rt decreased substantially compared to values during the early phase after the Japanese government implemented an enhanced quarantine control. Most recent estimates of Rt reached values largely below the epidemic threshold, indicating that a secondary outbreak of the novel coronavirus was unlikely to occur aboard the Diamond Princess Ship.

scholarly journals A New Infodemiological Approach Through Google Trends: Longitudinal Analysis of COVID-19 Scientific and Infodemic Names in Italy

2021 ◽  
Author(s):  
Alessandro Rovetta ◽  
Lucia Castaldo
Keyword(s):  
User Behavior ◽  
International Health ◽  
Google Trends ◽  
The Novel ◽  
Economic Activities ◽  
Online Tool ◽  
Health Authorities ◽  
Health Agencies ◽  
Long Time ◽  
Novel Coronavirus

Abstract COVID-19 has been classified by the scientific community as the worst pandemic in human history. The damage caused by the new disease was direct (e.g., deaths) and indirect (e.g., closure of economic activities). Within the latter category, we find infodemic phenomena such as the adoption of generic and stigmatizing names used to identify COVID-19 and the related novel coronavirus 2019 variants. These monikers have fostered the spread of health disinformation and misinformation, and fomented racism and segregation towards the Chinese population. In this regard, we present a comprehensive infodemiological picture of Italy from the epidemic outbreak in December 2019 until September 2021. In particular, we propose a new procedure to examine in detail the web interest of users in scientific and infodemic monikers linked to the identification of COVID-19. To do this, we exploited the online tool Google Trends. Our findings reveal the widespread use of multiple COVID-19-related names not considered in the previous literature, as well as a persistent trend in the adoption of stigmatizing and generic terms. Inappropriate names for cataloging novel coronavirus 2019 variants of concern have even been adopted by national health agencies. Furthermore, we also showed that early denominations influenced user behavior for a long time and were difficult to replace. For these reasons, we suggest that the assignments of scientific names to new diseases are more timely and advise against mass media and international health authorities using terms linked to the geographical origin of the novel coronavirus 2019 variants.

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